Rapid urban growth is becoming a serious problem in most developing countries. Tehran, the capital of
Iran, stands out as a vibrant metropolitan area, facing uncontrolled urban expansion. Public authorities
and decision makers require planning criteria regarding possible spatial developments. To monitor past
developmental trends and to simulate emerging spatiotemporal patterns of urban growth, this research
applies a geosimulation approach that couples agent-based modeling with multicriteria analysis (MCA)
for the period between 1986 and 2006. To model the major determinants controlling urban development,
three agent groups are defined, namely developer agents, government agents, and resident
agents. The behaviors of each agent group are identified by qualitative surveys and are considered separately
using multi-criteria analysis. The interactions of the agents are then combined through overlay
functions within a Geographic Information System (GIS). This analysis results in the creation of a propensity
surface of growth that is able to identify the most probable sites for urban development. Subsequently,
a Markov Chain Model (MCM) and a concise statistical extrapolation are used to determine
the amount of probable future expansion in Tehran. For validation purposes, the model is estimated
using 2011 data and then validated based on actual urban expansion. Given the accurate predictions
of the Markov Chain Model, further predictions were carried out for 2016 and 2026. This simulation
provides strong evidence that during the next decade planning authorities will have to cope with continuous
as well as heterogeneously distributed urban growth. Both the monitoring of growth and simulation
revealed significant developments in the northwestern part of Tehran, continuing toward the
south along the interchange networks.
Rapid urban growth is becoming a serious problem in most developing countries. Tehran, the capital ofIran, stands out as a vibrant metropolitan area, facing uncontrolled urban expansion. Public authoritiesand decision makers require planning criteria regarding possible spatial developments. To monitor pastdevelopmental trends and to simulate emerging spatiotemporal patterns of urban growth, this researchapplies a geosimulation approach that couples agent-based modeling with multicriteria analysis (MCA)for the period between 1986 and 2006. To model the major determinants controlling urban development,three agent groups are defined, namely developer agents, government agents, and residentagents. The behaviors of each agent group are identified by qualitative surveys and are considered separatelyusing multi-criteria analysis. The interactions of the agents are then combined through overlayfunctions within a Geographic Information System (GIS). This analysis results in the creation of a propensitysurface of growth that is able to identify the most probable sites for urban development. Subsequently,a Markov Chain Model (MCM) and a concise statistical extrapolation are used to determinethe amount of probable future expansion in Tehran. For validation purposes, the model is estimatedusing 2011 data and then validated based on actual urban expansion. Given the accurate predictionsof the Markov Chain Model, further predictions were carried out for 2016 and 2026. This simulationprovides strong evidence that during the next decade planning authorities will have to cope with continuousas well as heterogeneously distributed urban growth. Both the monitoring of growth and simulationrevealed significant developments in the northwestern part of Tehran, continuing toward thesouth along the interchange networks.
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